基于全局优化的中文事件同指消解方法

针对目前对事件同指关系的研究中多采用事件对分类或聚类方法而忽略事件相互之间内在联系的问题, 提出一个中文事件同指消解的全局优化模型, 用于减少因分类器错误造成的同指事件链不一致问题。该模型利用对称性、传递性、触发词、论元角色、事件距离等多种约束条件, 将同指消解转化成整数线性规划问题。实验结果表明, 与分类器方法相比, 全局优化模型的F1值提高4.20%。

Global Inference for Co-reference Resolution between Chinese Events

Currently, most pairwise resolution models for event co-reference focused on classification or clustering approaches, which ignored the relations between events in a document. A global optimization model for event co-reference resolution was proposed to resolve the inconsistent event chains in classifier-based approaches. This model regarded co-reference resolution as a integer linear program problem and introduced various kinds of constraints, such as symmetry, transitivity, triggers, argument roles, event distances, to further improve the performance. The experimental results show that the proposed model outperforms the local classifier by 4.20% in F1-measure.